Defining and Diagnosing Postpartum Clinical Endometritis and its Impact on Reproductive Performance in Dairy Cows
Bibliographic record
Abstract
The objectives of this study were to validate diagnostic criteria for clinical endometritis in postpartum dairy cows and to measure the impact of endometritis on reproductive performance. Data were collected from 1865 cows in 27 herds, including history of dystocia, twins, retained placenta, or metritis. All cows were examined once between 20 and 33 d in milk (DIM) including external inspection, vaginoscopy, and transrectal palpation of the cervix, uterus, and ovaries. All cows were followed for a minimum of 7 mo or until pregnancy or culling. Survival analysis was used to derive a case definition of endometritis based on factors associated with increased time to pregnancy. The significance of clinical findings depended on the interval postpartum when examination took place. The presence of purulent uterine discharge or cervical diameter > 7.5 cm after 20 DIM, or mucopurulent discharge after 26 DIM identified cows with clinical endometritis. Given vaginoscopy, no diagnostic criteria based on palpation of the uterus had predictive value for time to pregnancy. The prevalence of clinical endometritis was 16.9%. Vaginoscopy was required to identify 44% of these cases. Accounting for parity, herd, and ovarian status, cows with clinical endometritis between 20 and 33 DIM had a hazard ratio of 0.73 for pregnancy (took 27% longer to become pregnant), and were 1.7 times more likely to be culled for reproductive failure than cows without endometritis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".